The present disclosure generally relates to CMOS image sensors, and more specifically aims at the implementation of compressive sensing methods in a CMOS image sensor.
A CMOS image sensor generally comprises a plurality of pixels arranged in rows and in columns. Each pixel comprises a photodiode used in reverse mode, having its junction capacitance discharged by a photocurrent according to a received light intensity. The measurement of the illumination level received by a pixel is performed by measuring a quantity representative of the voltage across the photodiode at selected times, including the end of a so-called integration period, before and after which the pixel is reset by recharging of its photodiode.
Conventionally, in an image acquisition phase, for each pixel of the sensor, an output value representative of the illumination level received by the pixel during the integration is read, digitized, and stored in digital form. To decrease the quantity of digital data to be stored/processed downstream of the sensor, the acquisition phase is often followed by a phase of compressing the digitized image.
This conventional method of acquiring an entire digitized image, followed by a phase of compressing the digitized image, has several disadvantages. In particular, acquiring an entire digitized image is relatively long, which is a limitation for the increase of image acquisition rates. Further, such an acquisition of an entire digitized image results in a relatively high electric power consumption by the read and analog-to-digital conversion circuits of the sensor. Further, the phase of compressing the digitized image may comprise relatively complex calculations, and implies providing a digital signal processing unit dedicated to such a compression at the sensor output, possibly on the same chip as the sensor. These various disadvantages particularly raise an issue in systems with significant constraints relative to the acquisition and the compression of images in terms of processing speed and/or of electric power consumption.
To attempt partly overcoming these disadvantages, so-called compressive sensing methods have already been provided, where the compression phase is implemented in analog mode, upstream of the analog-to-digital converter(s), in combination with the acquisition phase. Compressive sensing methods enable to acquire and to simultaneously compress the image by providing, instead of reading and digitizing an output value representative of an illumination level individually received by each pixel, to make a plurality of non-coherent measurements, each based on a measurement support comprising a plurality of sensor pixels, for example, all the sensor pixels, or a subset of sensor pixels. Each measurement is a weighted sum of the brightness levels received by the different pixels of a measurement support. The weighting coefficients are randomly or pseudo-randomly generated. These coefficients may be binary (0 or 1), which makes the implementation of the weighted sum operations easier. To obtain a compressive effect, the total number of measurements made on the sensor is smaller than the total number of sensor pixels. It is thus possible to decrease the image acquisition time and the electric power consumption associated with the acquisition, particularly due to the fact that less data are read and digitized by the sensor. Further, digital compressive processing operations, subsequent to the acquisition, may be decreased or suppressed.
The original image can be reconstructed from the compressed image and the array of weighting coefficients used on acquisition. Such a reconstruction uses the sparseness of the original image in a specific decomposition base, for example, in a discrete cosine base or in a wavelet base.
Compressive sensing theories have been discussed in detail in various publications, for example, in article “An Introduction To Compressive Sensing” by Emmanuel J. Candès et al.
Further, CMOS image sensor architectures using compressive sensing have been described in articles “Block-Based Compressive Sensing in a CMOS Image Sensor”, by M. R. Dadkhah et al., and “CMOS Image Sensor With Per-Column ΣΔ ADC and Programmable Compressed Sensing” by Yusuke Oike et al.
There however is a need for a CMOS image sensor capable of implementing compressive sensing methods, this sensor at least partly improving certain aspects of prior art sensors. Particularly, there is a need for a CMOS sensor having a higher image acquisition speed and/or having a lower electric consumption, and/or having a decreased bulk as compared with prior art CMOS sensors using compressive sensing.